Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets

Background and objective: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Methods: Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Results: Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). Conclusion: The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller

An electroencephalographic investigation of the impact of eye movements in a change detection task

openIn studies involving Event-Related Potentials (ERPs), ocular artifacts such as blinks and saccades can compromise the quality of the recorded neural signals. To address this issue, researchers often manually reject epochs (that is a specific time-window extracted from the continuous EEG signal) containing these artifacts. However, this procedure consistently reduces the number of epochs that can be used for extracting ERPs. An alternative solution is to use Independent Component Analysis (ICA), which can preserve more epochs for analysis by removing only the artifact from the EEG recording. However, the reliability of ICA in neurocognitive studies of lateralized ERP components, such as the Sustained Posterior Contralateral Negativity (SPCN) related to visual working memory load, remains unclear, particularly in contexts where subjects are more likely to make saccades during the task. Furthermore, by using ICA, we are assuming that ocular movements do not interact with the neural signal, which has yet to be confirmed. For this reason, in the present experiment, all the participants were asked to perform a change detection task under two conditions: a ‘free gaze/saccade’ condition, where they were allowed to move their eyes to look at the lateralized stimuli, and a ‘fixation’ condition, where they were required to maintain the gaze on the center of the monitor. The subjects were also split into two groups, each performing the same experiment but with different stimulus presentation times (100 ms and 500 ms) to investigate whether saccades could differently affect the ERP in these conditions. The SPCN components were then extracted using both the Independent Component Analysis (ICA) correction and epoch-rejection methods. The results revealed that ICA correction is a robust and reliable method for experimental paradigms with a short presentation time of the stimuli (100 ms). By removing only the saccades, the features of the SPCN are preserved, suggesting that with this method we can retain a higher number of epochs for the ERP extraction with the certainty that saccades do not alter the neural signal.In studies involving Event-Related Potentials (ERPs), ocular artifacts such as blinks and saccades can compromise the quality of the recorded neural signals. To address this issue, researchers often manually reject epochs (that is a specific time-window extracted from the continuous EEG signal) containing these artifacts. However, this procedure consistently reduces the number of epochs that can be used for extracting ERPs. An alternative solution is to use Independent Component Analysis (ICA), which can preserve more epochs for analysis by removing only the artifact from the EEG recording. However, the reliability of ICA in neurocognitive studies of lateralized ERP components, such as the Sustained Posterior Contralateral Negativity (SPCN) related to visual working memory load, remains unclear, particularly in contexts where subjects are more likely to make saccades during the task. Furthermore, by using ICA, we are assuming that ocular movements do not interact with the neural signal, which has yet to be confirmed. For this reason, in the present experiment, all the participants were asked to perform a change detection task under two conditions: a ‘free gaze/saccade’ condition, where they were allowed to move their eyes to look at the lateralized stimuli, and a ‘fixation’ condition, where they were required to maintain the gaze on the center of the monitor. The subjects were also split into two groups, each performing the same experiment but with different stimulus presentation times (100 ms and 500 ms) to investigate whether saccades could differently affect the ERP in these conditions. The SPCN components were then extracted using both the Independent Component Analysis (ICA) correction and epoch-rejection methods. The results revealed that ICA correction is a robust and reliable method for experimental paradigms with a short presentation time of the stimuli (100 ms). By removing only the saccades, the features of the SPCN are preserved, suggesting that with this method we can retain a higher number of epochs for the ERP extraction with the certainty that saccades do not alter the neural signal

Detection of eye blink artifacts from single prefrontal channel electroencephalogram

Eye blinks are one of the most influential artifact sources in electroencephalogram (EEG) recorded from frontal channels, and thereby detecting and rejecting eye blink artifacts is regarded as an essential procedure for improving the quality of EEG data. In this paper, a novel method to detect eye blink artifacts from a single-channel frontal EEG signal was proposed by combining digital filters with a rule-based decision system, and its performance was validated using an EEG dataset recorded from 24 healthy participants. The proposed method has two main advantages over the conventional methods. First, it uses single channel EEG data without the need for electrooculogram references. Therefore, this method could be particularly useful in brain-computer interface applications using headband-type wearable EEG devices with a few frontal EEG channels. Second, this method could estimate the ranges of eye blink artifacts accurately. Our experimental results demonstrated that the artifact range estimated using our method was more accurate than that from the conventional methods, and thus, the overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased as compared to conventional methods. The MATLAB package of our library source codes and sample data, named Eyeblink Master, is open for free download. (C) 2015 Elsevier Ireland Ltd. All rights reserved.This work was supported in part by the ICT R&D program of MSIP/IITP [2014(KI10045461), Development of Multimodal Brain-Machine Interface System Based on User Intent Recognition], in part by the NRF grant funded by Korea Government (MSIP) (No. 2014R1A2A1A11051796), and in part by the KRISS-WCL project (Development of Next-generation Biomagnetic Resonance Technology). The authors would like to thank Dr. Do-Won Kim for his advice on statistical analysis